Artificial Intelligence and Machine Learning Engineering with Python (Paperback)
Idioma: inglés
Editorial: Independently Published, 2026
- Tapa blanda
- Nuevo
Librería: CitiRetail, Stevenage, Reino UnidoCitiRetail
Vendedor de IberLibro desde 29 de junio de 2022
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Paperback. Practical Artificial Intelligence and Machine Learning EngineeringMathematical Foundations, Intelligent Algorithms, Deep Learning Architectures, and Industrial AI Systems using PythonArtificial Intelligence and Machine Learning are transforming every industry-from manufacturing, healthcare, finance, cybersecurity, and telecommunications to autonomous systems, robotics, and intelligent enterprise applications. Yet many resources focus either on theory without implementation or coding without a solid mathematical foundation.This book bridges that gap.Practical Artificial Intelligence and Machine Learning Engineering is a comprehensive, industry-focused guide that takes you from core mathematical concepts to the design, development, deployment, and optimization of real-world AI systems using Python. Written for students, software engineers, data scientists, researchers, and technology professionals, this book combines rigorous theory with practical implementation techniques used in modern AI engineering environments.Inside this book, you will learn: Mathematical foundations for AI and machine learning, including linear algebra, probability, statistics, optimization, and information theorySupervised, unsupervised, semi-supervised, and reinforcement learning techniquesRegression, classification, clustering, dimensionality reduction, and ensemble learning algorithmsDeep learning architectures, including CNNs, RNNs, LSTMs, GRUs, Autoencoders, GANs, Transformers, and attention mechanismsNatural Language Processing (NLP), Large Language Models (LLMs), computer vision, and intelligent perception systemsFeature engineering, model evaluation, hyperparameter tuning, and performance optimization Scalable AI pipelines, MLOps practices, model deployment, monitoring, and lifecycle managementIndustrial AI system design for production-ready environmentsEthical AI, explainable AI, responsible machine learning, and governance frameworksEnd-to-end Python implementations using modern AI and machine learning librariesUnlike introductory AI books that stop at basic algorithms, this text emphasizes engineering principles required to build reliable, scalable, maintainable, and deployable AI solutions. Readers gain both conceptual understanding and practical skills needed for modern industrial applications.What Makes This Book Different?Strong mathematical rigor without unnecessary complexityPractical Python-based implementations and engineering workflowsCoverage from fundamentals to advanced deep learning systemsReal-world AI architecture and deployment considerationsIndustry-oriented approach suitable for professional developmentExtensive explanations, examples, and implementation strategiesWhether you are preparing for a career in Artificial Intelligence, Machine Learning Engineering, Data Science, Deep Learning, Intelligent Automation, or advanced software development, this book provides the knowledge and practical expertise required to design and build modern AI systems with confidence.Master the mathematics. Understand the algorithms. Build intelligent systems. Engineer production-ready AI solutions. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…
N° de ref. del artículo 9798180398024
- Título
- Artificial Intelligence and Machine Learning Engineering with Python (Paperback)
- Autor
- Ajai Kumar Medhavi
- Editorial
- Independently Published
- Año de publicación
- 2026
- Estado
- new
- Encuadernación
- Paperback
- Idioma
- inglés
- ISBN 13
- 9798180398024
Mathematical Foundations, Intelligent Algorithms, Deep Learning Architectures, and Industrial AI Systems using Python
Artificial Intelligence and Machine Learning are transforming every industry—from manufacturing, healthcare, finance, cybersecurity, and telecommunications to autonomous systems, robotics, and intelligent enterprise applications. Yet many resources focus either on theory without implementation or coding without a solid mathematical foundation.
This book bridges that gap.
Practical Artificial Intelligence and Machine Learning Engineering is a comprehensive, industry-focused guide that takes you from core mathematical concepts to the design, development, deployment, and optimization of real-world AI systems using Python. Written for students, software engineers, data scientists, researchers, and technology professionals, this book combines rigorous theory with practical implementation techniques used in modern AI engineering environments.
Inside this book, you will learn:
Mathematical foundations for AI and machine learning, including linear algebra, probability, statistics, optimization, and information theory
Supervised, unsupervised, semi-supervised, and reinforcement learning techniques
Regression, classification, clustering, dimensionality reduction, and ensemble learning algorithms
Deep learning architectures, including CNNs, RNNs, LSTMs, GRUs, Autoencoders, GANs, Transformers, and attention mechanisms
Natural Language Processing (NLP), Large Language Models (LLMs), computer vision, and intelligent perception systems
Feature engineering, model evaluation, hyperparameter tuning, and performance optimization
Scalable AI pipelines, MLOps practices, model deployment, monitoring, and lifecycle management
Industrial AI system design for production-ready environments
Ethical AI, explainable AI, responsible machine learning, and governance frameworks
End-to-end Python implementations using modern AI and machine learning libraries
Unlike introductory AI books that stop at basic algorithms, this text emphasizes engineering principles required to build reliable, scalable, maintainable, and deployable AI solutions. Readers gain both conceptual understanding and practical skills needed for modern industrial applications.
What Makes This Book Different?-
Strong mathematical rigor without unnecessary complexity
-
Practical Python-based implementations and engineering workflows
-
Coverage from fundamentals to advanced deep learning systems
-
Real-world AI architecture and deployment considerations
-
Industry-oriented approach suitable for professional development
-
Extensive explanations, examples, and implementation strategies
Whether you are preparing for a career in Artificial Intelligence, Machine Learning Engineering, Data Science, Deep Learning, Intelligent Automation, or advanced software development, this book provides the knowledge and practical expertise required to design and build modern AI systems with confidence.
Master the mathematics. Understand the algorithms. Build intelligent systems. Engineer production-ready AI solutions.
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